This invention relates generally to the control and management of automated manufacturing systems, and more particularly to a system and method for dynamically and incrementally monitoring and reporting the status of jobs in complex systems having long production paths and operations being executed in parallel.
In traditional systems, the planning and scheduling software (“the scheduler”, also more generally called the system control software) used to control automated manufacturing systems has no knowledge of the details of the execution of production schedules. The system control software is usually not informed about how well the scheduler's model (its belief about the system's current state, capabilities, timing constraints, etc.) reflects the reality of the actual system. At best, the software is told what parts of a schedule have been completed, what resources are currently available, and the values of a few variable timing parameters. This is illustrated in U.S. Pat. No. 5,826,104 to Riffin, cited above, which teaches an automated method to determine the real-time status of a currently running batch program on a mainframe computer system. Output from a tape management reporting system is processed so that relevant information pertaining to the progress of a currently running batch program is extracted and derived therefrom. Reports are generated that reveal the status of the batch program in terms of the processing time of the dataset associated with the batch program.
One example of an automated manufacturing system is a printing system, in which the only variable input information added to the models of the system are the availability of certain resources (e.g., if a feed tray is available) and the values of certain model parameters (e.g., the photoreceptor belt drift), and the feedback information is typically limited to what parts of a job have been completed, as shown in U.S. Pat. No. 5,696,893 to Fromherz et al. In Fromherz et al., a system is provided to allow for automated scheduling and completion of print jobs in a printing machine. A generic system for describing the functionality of various modules forming a print engine is provided for each of a plurality of subassemblies which form the printing machine. A component communicates description information about itself to a scheduling unit once it is integrated into a complete printing machine. The scheduling unit analyzes all functions available from various subassemblies and returns data representative of all available functions to the printing machine. However, the scheduler is not able to modify its models intelligently and react to failures of its plans. Also, any near failures caused by a combination of degraded execution over multiple modules cannot be detected easily, as no piece of software in the system has a global view of the execution.
This approach is unsatisfactory for complex systems having long production paths along which many operations are being executed in parallel and in different parts of the system. Neither the software nor the operator can make informed decisions about changes to the jobs, to schedules and to the execution of the jobs. For example, in the case of a failure, the scheduler needs to be able to decide whether and how to recover (e.g., by rerouting parts of the jobs being executed). In another example, if a job of priority higher than the currently executed jobs arrives, the scheduler needs to be able to reason about how to possibly change the current execution in order to accommodate the new job. Ideally, the software should also be able to take into account feedback from the actual execution of its schedules in order to improve its model and identify near-failures. (Model improvement may include, for example, updating constants to recently observed values, or removing unavailable operations. Near-failures could be used to plan more conservatively for components that seem at risk.)
In the presence of problems that arise out of the interaction of multiple modules, where each module sees only small deviations but the cumulative effect is large,system-level reporting would provide a better basis to pinpoint the source of a problem than local reporting. (For example, in the case of cumulative delays, the module reporting the fault may exhibit the effects of the accumulated delay, but an earlier module may have been the actual source of the problem.) Such an approach could also permit tight scheduling in the presence of steps whose duration or results cannot be precisely predicted in advance.
It is desirable to provide a better basis for operator decisions, failure recovery, and preemptive scheduling in complex systems, as well as to enable model adaptation and the reporting of (near) failures through reporting relevant information, transforming that information to an appropriate internal state, and querying that state for various purposes.